This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.
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This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with GitHub, and reproducible document preparation with R markdown.
A. Park, R. Quadari, and H. Tsang. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), page 680-684. (October 2017)
M. Aurnhammer, P. Hanappe, and L. Steels. 5th International Semantic Web Conference, ISWC 2006, Athens, GA, USA, volume 4273 of Lecture Notes in Computer Science, page 58--71. Springer-Verlag, (5-9 nov 2006)